Detecting faults in subscriber telephone lines

ABSTRACT

A method tests a subscriber line. The method includes providing a classifier to predict a performance characteristic of the line based at least in part on a value of an auxiliary variable. Each value of the auxiliary variable represents a property of the line. The method also includes performing electrical measurements on the subscriber line, using the measurements to predict a value of the auxiliary variable, and predicting the performance characteristic of the line. The act of predicting the characteristic applies the classifier to the predicted value of auxiliary variable.

This application is a Division of Ser. No. 09/410,237 filed Sep. 30,1999.

BACKGROUND OF THE INVENTION

This application relates generally to communications networks, and moreparticularly, to detecting faults in communication lines.

Recently, there has been an increased demand for the subscriber lines ofplain old telephone services (POTS's) to carry high-speed digitalsignals. The demand has been stimulated by home access to both theInternet and distant office computers. Both types of access typicallyemploy a POTS line as part of the path for carrying digital signals.

POTS's lines were built to carry voice signals at audible frequenciesand can also carry digital signals as tone signals in the near audiblefrequency range. Modern digital services such as ISDN and ADSL transmitdata at frequencies well above the audible range. At these higherfrequencies, POTS's lines that transmit voice signals well may transmitdigital signals poorly. Nevertheless, many telephone operating companies(TELCO's) would like to offer ISDN and/or ADSL data services to theirsubscribers.

Telephone lines between a TELCO switch and subscribers' premises arefrequent sources of poor performance at the high frequenciescharacteristic of ISDN and ADSL transmissions. Nevertheless, high costhas made widespread replacement of these subscriber lines an undesirablesolution for providing subscribers with lines capable of supporting ISDNand ADSL. A less expensive alternative would be to repair or remove onlythose subscriber lines that are inadequate for transmitting high-speeddigital data.

To limit replacement or repair to inadequate lines, TELCO's have placedsome emphasis on developing methods for predicting which subscriberlines will support data services, such as ISDN and ADSL. Some emphasishas been also placed on predicting frequency ranges at which such dataservices will be supported. Some methods have also been developed forfinding faults in subscriber lines already supporting data services sothat such faults can be repaired.

Current methods for predicting the ability of subscriber lines tosupport high-speed digital transmissions are typically not automated,labor intensive, and entail test access at multiple points. Often, thesemethods entail using skilled interpretations of high frequencymeasurements of line parameters to determine data transmissionabilities. At a network scale, such tests are very expensive toimplement.

The present invention is directed to overcoming or, at least, reducingthe affects of one or more of the problems set forth above.

SUMMARY OF THE INVENTION

In a first aspect, the invention provides a method of testing asubscriber line. The method includes providing a classifier to predict aperformance characteristic of the line based at least in part on a valueof an auxiliary variable. Each value of the auxiliary variablerepresents a property of the line. The method also includes performingelectrical measurements on the subscriber line, using the measurementsto predict a value of the auxiliary variable, and predicting theperformance characteristic of the line. The act of predicting thecharacteristic applies the classifier to the predicted value ofauxiliary variable.

In a second aspect, the invention provides a method of detecting acondition or fault in a subscriber line. The method includes classifyingthe line as a nominal line or a non-nominal line from electricalmeasurements. The method determines whether the line has the conditionor fault from the electrical measurements and the classification.

In a third aspect, the invention provides a method for detecting a faultin a subscriber line. The method includes making a preliminaryclassification of the line as qualified or disqualified for a dataservice line in response to electrical measurements thereon. The methodalso includes determining whether the line has a fault from theelectrical measurements and the preliminary classification.

In a fourth aspect, the invention provides a method of detecting a faultin a subscriber line. The method includes making a preliminaryclassification of the line as qualified or disqualified for a dataservice line in response to electrical measurements thereon. The methodalso includes determining whether the line has a fault from theelectrical measurements and the preliminary classification.

In a fifth aspect, the invention provides a method of creating a stackof classifiers for detecting line faults. The method includes selectinga learning set of subscriber lines and determining the form of theclassifier from values of features and auxiliary variables of the lines.A portion of the lines have the fault, and a portion of the lines do nothave the fault. The value of the auxiliary variable determines whetherthe associated line is one of nominal and qualified for a data service.

In a sixth aspect, the invention provides a program storage devicestoring a computer executable program of instructions for performing oneor more of the above-described methods.

Various embodiments use test accesses, which provide data on lowfrequency electrical properties of subscriber lines, to make predictionsabout high frequency performance.

BRIEF DESCRIPTION OF THE DRAWINGS

Other features and advantages of the invention will be apparent from thefollowing description taken together with the drawings in which:

FIG. 1 shows a portion of a POTS network having a system for detectingfaults in subscriber telephone lines;

FIG. 2A shows a first measuring setup for making one-ended electricalmeasurements on a subscriber telephone line;

FIG. 2B is an equivalent circuit for the measuring setup of FIG. 2A;

FIG. 2C shows a second measuring setup for making one-ended electricalmeasurements on a subscriber telephone line;

FIG. 3 illustrates signal distortions produced by the test bus andstandard voice test access;

FIG. 4 shows a split pair fault in a subscriber line;

FIG. 5 shows how a splice error can produce a split pair fault;

FIG. 6A shows a phase measurement signature of a resistive imbalance ona subscriber line;

FIG. 6B shows a phase measurement signature of a split pair fault on asubscriber line;

FIG. 7 is a flow chart illustrating a method of detecting faults onsubscriber lines with the system of FIGS. 1, 4, and 5;

FIG. 8 is a flow chart illustrating a method of qualifying subscriberlines with the method of FIG. 7;

FIG. 9 shows a method of providing high speed data services using themethods of FIGS. 7 and 8;

FIGS. 10A-10E show exemplary subscriber lines having different gaugemixes;

FIG. 11 shows a subscriber line with a bridged tap;

FIGS. 12A-12E shows exemplary structures of subscriber lines having onebridged tap;

FIG. 13 is a flow chart for a method of determining the specificphysical structure of a subscriber line from a reference set;

FIG. 14 is a flow chart for a method of finding a best match between asubscriber and model lines;

FIG. 15 is a flow chart for a method of qualifying subscriber lines; and

FIG. 16 is a flow chart for a business method of providing high-speeddata services to subscribers.

FIG. 17 is a flow chart for a stacked method of detecting bridged tapsusing auxiliary variables;

FIG. 18A shows predicted and actual signal attenuations of nominalsubscriber lines;

FIG. 18B shows predicted and actual signal attenuations of non-nominalsubscriber lines;

FIG. 18C shows predicted, shifted predicted, and actual signalattenuations for an exemplary nominal subscriber line;

FIG. 19 shows an exemplary decision tree;

FIG. 20 illustrates the action of the rules of the decision tree of FIG.19 on a set of subscriber lines;

FIG. 21 is a flow chart illustrating a method of creating the decisiontrees with machine learning methods; and

FIG. 22 is a flow chart for a method of determining the branching rulesof the decision tree illustrated in FIGS. 19-20.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

Measurement and Test Apparatus

FIG. 1 shows a portion of a POTS network 10 that has a system 11 fordetecting faults in subscriber lines 12-14. The subscriber lines 12-14connect subscriber units 16-18, i.e., modems and/or telephones, to atelephony switch 15. The switch 15 connects the subscriber lines 12-14to the remainder of the telephone network 10. The switch 15 may be aPOTS switch or another device, e.g., a digital subscriber loop accessmultiplexer (DSLAM).

Each subscriber line 12-14 consists of a standard twisted two-wiretelephone line adapted to voice transmissions. The two wires aregenerally referred to as the ring AR@ and tip AT@ wires.

A large portion of each subscriber line 12-14 is housed in one or morestandard telephone cables 22. The cable 22 carries many subscriber lines12-14, e.g., more than a dozen, in a closely packed configuration. Theclose packing creates an electrical environment that changestransmission properties of the individual subscriber lines 12-14.

Electrical measurements for detecting line faults are performed by ameasurement unit 40. In various embodiments, the measurement unit 40includes one or both devices 41 and 43. Each device 41, 43 performsone-ended electrical measurements on selected lines 12-14. In preferredembodiments, the electrical measurements are one-ended. The device 41performs measurements on tip and ring wires of a selected subscriberline 12-14 in a common mode configuration and produces results usefulfor detecting split pairs. The, device 43 can measure admittances of thetip and ring wires of a selected line 12-14 either separately ortogether and produces data useful for determining the specific physicalline structure. The measurement unit 40 may also house other devices(not shown) for performing other types of electrical measurements, i.e.,one-ended or two-ended measurements. The measurement unit 40 couples tothe switch 15 via a test bus 42.

The devices 41, 43 connect to the switch 15 through the test bus 42 anda standard voice test access 44. The voice test access 44 electricallyconnects either the device 41 or device 43 to the subscriber lines 12-14selected for testing. The voice test access 44 generally transmitselectrical signals with low frequencies between about 100 Hetz (Hz) and20 kilo Hz (KHz). But, the test access 44 may transmit signals at higherfrequencies, e.g., up to 100 to 300 KHz, in some switches 15.

The measurement unit 40 is controlled by computer 46, which selects thetypes of measurements performed, the device 41, 43 used, and thesubscriber lines 12-14 to test. The computer 46 sends control signals tothe measurement unit 40 via a connection 48, e.g., a line, network, ordedicated wire, and receives measurement results from the measurementunit 40 via the same connection 48.

The computer 46 contains a software program for controlling line testingby the measurement unit 40 and for detecting line conditions or faultswith results from the measurement unit 40. The software program isstored, in executable form, in a data storage device 49, e.g., a harddrive or random access memory (RAM). The program may also be encoded ona readable storage medium 50, such as an optical or magnetic disk, fromwhich the program can be executed.

To perform a test, the measurement unit 40 signals the voice test access44 to connect the line 12-14 to be tested to wires of the bus 42 forconnecting to internal devices 41, 43. Then, one or both of the internaldevices 41, 43 performs electrical measurements on the selected line12-14. After the measurements are completed, the measurement unit 40signals the switch 15 to disconnect the line 12-14 from the wires of thebus 42.

The computer 46 can classify selected subscriber lines 12-14 prior tofully connecting the lines 12-14 for data services. The range ofpossible classes to which a line 12 14 can be assigned will depend onthe business needs of a TELCO. A simple, but very useful set of classesis “qualified” and “disqualified” to provide data services.Qualification is based on determining, with high certainty, that aselected line 12-14 will support a specified data service.Disqualification is based on determining, with high certainty, that theselected line 12-14 will not support the specified data service.

FIG. 2A shows a first setup 52 for performing one type of one-endedelectrical measurements with the device 41. The measurements are used todetect faults such as split pairs in the subscriber lines 12-14 of FIG.1.

The device 41 has a variable frequency voltage source 54 for driving thetip and ring wires T, R of the subscriber line 12-14 under test. Thevoltage source drives both wires together, i.e., in a common modeconfiguration, at a frequency controlled by the measurement unit 40. Thetip and ring wires T, R of the line 12-14 under test are connected tothe device 41 via the voice test access 44.

The voltage source 54 connects to one side of resistors R₁ and R₂. Thesecond side of resistors R₁ and R₂ connect to the respective tip andring wires T, R of the subscriber line 12-14 under test. Thus, thevoltage source 54 drives the tip and ring wires T, R in common modethrough the corresponding resistors R₁ and R₂.

The resistors R₁ and R₂ have equal resistances so that the voltagesource 54 induces equal voltages V₁, V₂ between each resistor R₁, R₂ andground if the currents I_(T), I_(R) therein are also equal. Differencesin the input impedances Z_(T), Z_(R) of the tip and ring wires T, R makethe voltages V₁, V₂ differ in amplitude and/or phase. For example,mutual inductance effects produced by a split pair can make the inputimpedances Z_(T), Z_(R) unequal.

Voltmeters VM₁ and VM₂ measure amplitudes and phases of voltages V₁ andV₂, respectively. From measurements of the voltmeters VM₁ and VM₂, thecomputer 46 can obtain the phase difference between V₁ and V₂.

FIG. 2B shows an equivalent circuit 55 for the measurement setup 52 ofFIG. 4. In the common mode configuration, the tip and ring wires T, Ract as elements of independent circuits 56, 57 that connect the voltagesource 54 to a common ground 58. The tip wire T is equivalent to animpedance Z_(T) in the circuit 56, and the ring wire R is equivalent toan impedance Z_(R) in the circuit 57.

The input impedances Z_(T) and Z_(R) may have different amplitudesand/or phases due to the presence of a fault on either the tip or ringwires T, R. Different values for Z_(T) and Z_(R) produce differentcurrents I_(T) and I_(R) in the circuits 56 and 57 and differentmeasured voltages V₁ and V₂. The phase of the voltage difference V₁-V₂is proportional to the phase difference between the input impedances ofthe tip and ring wires T, R. In the phase of the difference V₁-V₂,termination effects associated with the attached subscriber unit 16 canlargely be ignored.

FIG. 2C shows a measuring setup 60 for performing one-ended electricalmeasurements on a selected subscriber line 12-14 with the device 43shown in FIG. 1. The device 43 measures electrical properties, which canbe used to determine the specific physical structure of the lines 12-14and to determine line conditions and faults as is described below. Somemethods for detecting line faults and conditions with the device 43 havebeen described in U.S. application Ser. No. 09/294,563 ('563), filedApr. 20, 1999. The '563 application is incorporated herein, byreference, in its entirety.

The device 43 is adapted to measure admittances between the tip wire T,ring wire R, and ground G for a subscriber line 12-14 being tested. Thetip and ring wires T, R of the line 12-14 being tested couple to drivingvoltages V₁′ and V₂′ through known conductances G_(t) and G_(r). The tipand ring wires T, R also connect to voltmeters V_(t) and V_(r). TheV_(t) and V_(r) voltmeters read the voltage between the tip wire T andground G and between the ring wire R and ground G, respectively. Thereadings from the voltmeters V_(t) and V_(r) enable the computer 46 todetermine three admittances Y_(tg), Y_(tr), and Y_(rg) between the pairstip-ground, tip-ring, and ring-ground, respectively. The device 43 canmeasure the admittances at preselected frequencies in the rangesupported by the voice test access 44. The '563 application hasdescribed methods for performing such measurements.

Referring to FIG. 3, the computer 46 may compensate for signaldistortions introduced by the test bus 42 and/or the voice test access44. To perform compensation, the computer 46 treats the two lines of thecombined bus 42 and test access 44 as a linear two port systems. Then,the currents and voltages I_(T)′, V_(T)′ and I_(R)′, V_(R)′ at theoutput terminals of the measurement device 40 are related to thecurrents and voltages I_(T), V_(T) and I_(R), V_(R) on the outputterminals of the tip and ring wires T, R by the following 2×2 matrixequations:[I _(T) , V _(T) ]=A(f)[I _(T) ′, V _(T)′]^(t)and[I _(T) , V _(T) ]=A′(f) [I _(R) ′, V _(R)′]^(t).The frequency dependent matrices A(f) and A′ (f) are determinedexperimentally for each bus 42 and voice test access 44. Then, thecomputer 46 calculates the impedances or admittances of the tip and ringwires T, R with the currents and voltages I_(T), V_(T) and I_(R), V_(R)obtained from the above equations.

The measurement unit 40 and computer 46 can detect faults such as splitpairs, resistive imbalances, metallic faults, load coils, bridged taps,gauge mixtures, and high signal attenuations. Co-pending U.S. patentapplication Ser. No. 09/285,954 ('954), filed Apr. 2, 1999, describesthe detection of some of these faults and is incorporated herein byreference in its entirety.

Split Pairs

Referring again to FIG. 1, close proximity can inductively produce crosstalk between the subscriber lines 12-14. Cross talk is frequently causedby large noise or ringing signals on one of the lines 12-14. The largesignal inductively produces signals on nearby lines 12-14. To reducecross talk, the tip and ring wires T, R of each subscriber line 12-14are either tightly twisted together or kept in close proximity in thecable 22. In this way, stray signals affect both wires of a pair so thatinduced signals do not impact the difference signal between the tip andring wires.

Referring to FIG. 4, the tip and ring wires T′, R′ of a subscriber line24 are separated spatially in a portion of cable 26. The portion of thesubscriber line 24 in which the tip and ring wires T′, R′ are spatiallyseparated is referred to as a split pair. A split pair T′, R′ has a highrisk of picking up cross talk other lines 28-29 in the same cable 26 orexternal noise sources such as power lines (not shown).

Split pairs also introduce impedance discontinuities into subscriberlines, because the split pair creates a localized and abrupt impedancevariation. Impedance discontinuities can cause signal reflections andhigh signal attenuations for high-speed digital transmissions.

FIG. 5 illustrates one type of split pair, i.e., a split pair caused bya splice error. The splice error occurred when two portions of asubscriber line 32, which are located in two different cables 33, 34,were joined. The splice 35 has joined tip and ring wires T₁, R₂ from twodifferent twisted pair lines 36, 37 in the cable 33 to tip and ringwires T₃, R₃ of a single twisted pair 38 in the adjacent cable 34. Thetip and ring wires T₁, R₂ of the portion of the subscriber line 32 arewidely separated in a substantial portion of the cable 33. Thus, the tipand ring wires T₁, R₂ form a split pair.

Detection of split pair faults is difficult for several reasons. First,split pairs do not produce easily detected effects such as metallicfaults, i.e., broken wires or shorted wires, or impedance imbalances.Second, split pairs produce cross talk that produce intermittent faultsdepending on the signals on nearby lines, e.g., intermittent ringingsignals. The intermittency makes such faults difficult to recognize.

Conventional tests have not been very successful in detecting splitpairs. Nevertheless, split pairs can degrade the quality of a subscriberline for high-speed data services.

FIGS. 6A and 6B provide graphs 68, 69 of the phase of the voltagedifference V₁-V₂ between resistors R₁ and R₂ while testing two exemplarysubscriber lines 12-14 with the measurement setup 52 of FIG. 4. Thegraphs 68, 69 provide frequency sweeps of the phase difference, whichshow signatures of faults that can interfere with high-speed dataservices, e.g., ISDN or ADSL.

Referring to FIG. 6A, the graph 68 shows a signature for a resistiveimbalance fault on the tested subscriber line 12-14. The signature for aresistive imbalance is a pronounced peak in the phase of the voltagedifference V₁-V₂. The peak appears in the phase difference betweenimpedances of the tip and ring wires. The peak has a narrow width thatis typically not more than a few hundred to about 2 KHz. Typically, thephase has a height of greater than about 5°.

Referring to FIG. 6B, the graph 69 shows a signature for a split pairfault on the tested subscriber line 12-14. The signature is a flat andsubstantially constant phase for V₁-V₂, i.e., a substantially constantnon-zero phase difference between the input impedances Z_(T), Z_(R) ofthe wires T, R. Typically, the phase has a value of between about 0.5°and 1.5°. The nonzero and flat phase extends over a region offrequencies having a width of at least 5,000 kilo Hz. The phase of Z_(T)and Z_(R) may remain flat, nonzero, and peakless from about 100 Hz toabout 20,000 Hz if a split pair is present, i.e., over the frequencyrange measurable through the voice test access 44, shown in FIG. 1. Anonzero and substantially frequency independent phase difference betweenthe input impedances Z_(T), Z_(R) of the tip and ring wires is asignature for a split pair on the subscriber line 12-14 being tested.

FIG. 7 is a flow chart illustrating a method 70 of detecting a fault inthe subscriber lines 12-14 with the system 11 of FIG. 1. The computer 46selects the subscriber line 12-14 to test for faults (step 72). Themeasurement unit 40 electrically connects to the selected line 12-14 viathe voice test access 44 of the TELCO switch 15 (step 74). Theconnection produces the measurement setup 52 illustrated in FIGS. 4 and5.

The measurement unit 40 performs one-ended electrical measurements todetermine a signal proportional to the phase difference of the inputimpedances Z_(T), Z_(R) of the tip and ring wires of the selected line12-14 (step 76). The quantity actually measured is the phase of V₁-V₂,which is proportional to the phase of the difference of the inputimpedances Z_(T), Z_(R). The device 41 measures the phase by driving thetip and ring wires in the common mode configuration shown in FIG. 4. Thedriving frequencies are between about 100 Hz to 20,000 kilo Hz andaccessible via the voice test access 44. Such frequencies are very lowcompared to transmission frequencies of high-speed data services such asISDN and ADSL.

The computer 46 analyzes the measurements of the phase as a function offrequency to determine whether the phase has a signature for a linefault (step 78). The line faults that produce signatures in the phaseinclude split pairs and resistance imbalances as described above inrelation to FIGS. 6B and 6A, respectively. Other signatures arepossible, e.g., for other types of faults. If a signature for a linefault is found, the computer 46 identifies that a fault has beendetected (step 80). The identification may entail making a reportingact. The reporting act may include making an entry in a file that liststhe faults on the subscriber lines 12-14, displaying a warning on anoperator's display screen 47 or on a screen of a service technician (notshow), or informing a program that allocates subscriber lines 12-14. Ifno signatures for line faults are found, the computer 46 identifies theabsence of the line faults associated with signatures for the selectedline 12-14, e.g., by performing a reporting act (step 82).

FIG. 8 is a flow chart illustrating a method 90 for a test thatdetermines whether the subscriber lines 12-14 of FIG. 1 qualify ordisqualify for a high-speed data service. To start a test, an operatoror the computer 46 selects a subscriber line 12-14 (step 92). Theoperator or computer 46 also selects the type of data service for whichthe selected subscriber line 12-14 is to be tested (step 94). Forexample, the types of service may be ISDN or ADSL. After selecting theline 12-14 and service type, the measurement unit 40 performs one-endedelectrical measurements to detect preselected types of faults in theselected line 12-14 (step 96). The one-ended measurements include testsaccording to the method 70 of FIG. 7 to detect split pairs.

The other types of line faults and conditions, which are selected fortesting, depend on the types and speeds of data services, the propertiesof the switch 15, and the type of modem to be used. Frequently, testscheck for high signal attenuations, resistive imbalances, and thepresence of load coils, metallic faults, or bridged taps, because theseconditions and faults can disqualify a line for high-speed data service.But, line qualification tests may also check for capacitive imbalances,and above-threshold noise levels, because these conditions can alsoaffect qualification results. Methods and apparatus for detecting someof these conditions and faults are described in co-pending patentapplications.

One such application is U.K. Patent Application No. 9914702.7, titled“Qualifying Telephone Lines for Data Transmission”, by Roger Faulkner,filed Jun. 23, 1999, which is incorporated herein by reference, in itsentirety. Other such co-pending applications include the above-mentioned'954 and '563 patent applications.

If one of the preselected types of faults or line conditions isdetected, the computer 46 reports that the selected subscriber line12-14 is disqualified for the selected data transmissions (step 98).Otherwise, the computer 46 reports that the selected line 12-14qualifies for the selected data service (step 100).

To report the tested line's status, the computer 46 makes an entry in alist stored in the storage device 49. The list identifies the line, dataservice, and qualification or disqualification status. The computer 46may also report is the line's status by displaying a disqualification orqualification signal on the display screen 47 visible to an operator.

FIG. 9 is a flow chart for a method 101 used by a TELCO to provide ahigh-speed data service, e.g., ISDN or ADSL, to telephone subscribers.The TELCO programs the computer 46 of FIG. 1 to automatically selectindividual subscriber lines 12-14 connected to the local switch 15 (step102). In response to selecting the line 12-14, the voice test access 44connects the selected line 12-14 to the measurement unit 40 for testing(step 104). The measurement unit 40 connects the selected line 12-14 tothe measurement device 41 and may also connect the selected line 12-14to other internal measurement devices (not shown). The computer 46 andmeasurement unit 40 determine whether the selected line 12-14 has asplit pair and qualifies for the data service according to the methods70, 90 of FIGS. 7 and 8 (step 106). Next, the computer 46 updates a listrecording the identities of lines 12-14 that qualify and of lines 12-14having split pairs (step 108). The computer 46 waits a preselected timeand restarts the testing for another of the lines 12-14 at step 102.

The TELCO regularly checks the list to determine whether any of thelines 12-14 have split pairs (step 110). If a line has a split pair, theTELCO performs a business action based on the presence of the split pairfault (step 112). The business action may include sending a worker torepair or replace the affected line 12-14, designating the affected line12-14 as unable to transmit data, or setting a lower billing rate basedon the presence of the fault.

The TELCO also regularly checks the list to determine whether any of thelines 12-14 qualify for the high-speed data service (step 114). Inresponse to finding that one or more of the lines 12-14 qualify, theTELCO performs a business action related to the line's qualification(step 116). For example, the TELCO may offer the high speed data serviceto subscribers who have the lines 12-14 qualified for the data serviceand who do not presently subscribe to the data service.

Specific Physical Structure of Subscriber Lines

Referring again to FIG. 1, the subscriber lines 12-14 may have widelydifferent physical structures. A line's specific physical structure isdescribed by properties such as line length, gauge or gauges, andcontent of bridge taps. Interpretations of electrical measurements toobtain line transmission properties such as the signal attenuation aredependent upon the specific physical line structure. Thus, knowing thespecific physical structure of a subscriber line aids in predicting howwell the line 12-14 will support high speed digital data services, e.g.,to predict maximum data speeds.

FIGS. 10A-E illustrate parameters that describe gauge mix parametersthrough exemplary lines 121-125 in which drawing widths represent wiregauges. The lines 121, 122 have uniform structures described bydifferent wire gauges. The lines 124, 125 have segmented structures inwhich adjacent segments have different wire gauges, i.e., mixtures ofgauges. The gauge composition of these lines 124, 125 is described bysegment lengths and segment gauges. The structures are also described bythe serial layout of the segments. The line 123 has different tip andring wires T₄, R₄ and is described by the gauges of the T₄ and R₄ wires.

Referring now to FIG. 11, a subscriber line 127 has an extra twistedwire pair 128 spliced onto the line 127. The spliced on wire pair 128 isreferred to as a bridged tap. The existence or absence of bridged tapsis a parameter that also influences how well the subscriber line 127will support high-speed digital data services.

In the United States, many subscriber lines have bridged taps because ofthe way in which telephone lines were laid out in housing subdivisions.Telephone lines were laid out prior to determining the exact positioningof the houses of the subdivisions. The lines ran near planned positionsof several houses. When the houses were later built, the builderconnected the telephone units to the nearest point on one of theoriginally laid telephone lines. Unconnected portions of the originallines produced bridged taps.

The bridged tap 128 reflects signals from termination 129. The reflectedsignals then travel back to the subscriber line 127 and interfere withsignals on the subscriber line 127. The most harmful interference occurswhen the reflected signal is out of phase with the incoming signal. Insuch a case, the reflected signal destructively interferes with theincoming signal on the subscriber line 127.

The length of the bridged tap 128 determines the phase differencebetween the original and reflected signals. For high-speed digitalsignals whose frequencies extend to about 1 mega Hetz (MHz), e.g., ADSLsignals, a substantial cancellation can occur if the bridged tap 128 hasa length between about 200 to 700 feet. In the United States, thebridged taps left over from the construction of many housingsubdivisions have lengths in this range. Thus, the ability to detect andremove the bridged tap 128 is useful to TELCO's that want to offerhigh-speed digital data services to their subscribers.

FIGS. 12A-12E illustrate structure parameters that describe bridged taps130, 134 through exemplary subscriber lines 135-139. The lines 135, 136have bridged taps 130, 131 described by different physical lengths. Thelines 137-138 have bridged taps 132, 133 described by differentlocations along the lines 137, 138. The line 139 has a bridged tap 134,which is at least partially described by its location along a particularsegment of the line 139. Finally, the lines 136, 139 have bridged taps131, 134 described by different gauges.

To determine the specific physical structures of unknown subscriberlines, a reference set of model lines may be employed. A reference setis an ensemble of model lines with different and known specific physicalstructures. To determine the specific physical structure of an unknownsubscriber line, measured properties of the unknown line are compared tothe same properties in model lines. If a match is found, the unknownline has the same specific physical structure as the matching modelline.

Reference data on the specific physical structures of the model linesmay be compiled in either a reference data file or a set of referenceequations. Both the reference data file and the set of referenceequations index the individual model lines by values of a preselectedset of measurable electrical properties. In some embodiments, thepreselected electrical properties are the frequency-dependentadmittances measurable with the device 43 of FIG. 2C.

The content of model lines in the reference set may be tailored to theexpected structures of the unknown subscriber lines. For example, if theunknown lines do not have bridged taps, the reference set might not havemodel lines with bridged taps. On the other hand, if the unknown linesmay have bridged taps, the reference set includes some model lines withbridged taps. Knowledge of the practices used to lay out the subscriberlines under test can help to determine the best content of model linesfor the reference set. For different subscriber line populations,reference sets can be selected empirically or based on human knowledge.

Typically, the reference set includes model lines having uniformlyvarying values of the parameters described in relation to FIGS. 10A-10Eand 12A-12E. The model lines have a distribution of lengths and mayinclude one, two, or three segments with zero, one, or two bridged taps,and a distribution of subscriber termination loads. The segments andbridged taps can have varying lengths, locations, and gauges.

FIG. 13 is a flow chart for a method 140 of determining the specificphysical line structure of the subscriber lines 12-14 of FIG. 1 from areference set of model lines. To start, an operator or the computer 46selects a subscriber line (ssl) to test (step 142). The computer 46directs the measuring unit 40 to perform preselected one-endedelectrical measurements on the selected subscriber line over a range offrequencies (step 144).

In one embodiment, the electrical measurements are one-ended andperformed with the device 43, shown in FIG. 2C. During the measurements,the voltage source 54 drives the tip and/or ring wires of the selectedsubscriber line 12-14 with voltage sources V₁′, V₂′. The drivingfrequency is swept over a range, e.g., from about 100 Hetz to about20,000 to 40,000 Hetz, and one or more of the admittances Y_(tg),Y_(tr), Y_(rg) are measured for various driving frequencies. Themeasurements provide complex input admittances, i.e., amplitudes andphases for a preselected set of frequencies “f”.

After performing the measurements, the computer 46 searches for a “best”match between model lines belonging to the reference set and theselected subscriber line (step 146). The search for matches involvescomparing preselected electrical properties of the selected subscriberline to the same properties for the model lines. For the selectedsubscriber line, the values of the preselected electrical properties areobtained from the one-ended electrical measurements. For the modellines, the values of the same electrical properties are either looked upfrom a file in the data storage device 49 or calculated from a set ofreference equations. The comparison determines which model line “best”matches the selected subscriber line.

The computer 46 identifies a specific physical line structure for theselected subscriber line 12-14 has the same form as the specificphysical line structure of the “best” matching model line (step 148).Identifying the specific physical line structure may include reportingthe structure, e.g., displaying values of parameters for the specificphysical structure to a operator, writing the values to a file, orproviding the values to a software application. For example, thesoftware application may use the match information to qualify ordisqualify the selected line 12-14. The parameters may provide gaugemixtures and tap locations and positions.

For the model lines, the specific physical structures are either storedin the same file listing the electrical properties of the model lines ordetermined from the reference equations. Actual values of the electricalproperties and structure parameters of the model lines are obtainedprior to testing the subscriber line by analytic calculations orexperimentation.

In a preferred embodiment, the computer 46 finds the “best” matchingmodel line by calculating an error function for each model line (ml).The error function has one of two forms E or E′ given by:E=Σ _(f) W(f)|M _(ml)(f)−M _(sl)(f)| and E′=Σ _(f) W(f)|M _(ml)(f)−M_(sl)(f)|^(2Q).M_(ml)(f) and M_(ssl)(f) are the values of the preselectedfrequency-dependent electrical properties of the model line (ml) and theselected subscriber line (ssl), respectively. Q and W(f) define the formof the error functions, i.e., E or E′. Q is a fixed integer, e.g., 1 or2. W(f) is positive definite weight function, e.g., a function offrequency “f” or a constant.

In some embodiments, the preselected electrical properties M_(ml)(f),M_(ssl)(f) are the phases of one or more complex admittances of thelines ssl, ml. Various embodiments employ either the phase of thetip-to-ground admittance Y_(tg), the phase of the ring-to-groundadmittance Y_(rg), and/or the phase of the tip-to-ring admittanceY_(tr). If the tip-to-ground or ring-to-ground admittances Y_(tg),Y_(rg) are used, many termination effects due to the subscriber units16-18 of FIG. 1 are not seen. The phase of these admittances is oftensmall, e.g., 4° or less, and approximately equals the ratio of theimaginary to real parts of the admittance. For such a case and Q=1, theerror function E′ is:E′=Σ _(f) [Im(admittance)_(ml) /Re(admittance)_(ml)−Im(admittance)_(ssl) /Re(admittance)_(ssl)]².

In another embodiment, the preselected electrical properties M_(ml)(f),M_(ssl)(f) are the full complex admittances of the lines ssl, ml, i.e.,Y_(tg), Y_(rg), and/or Y_(tr). Using the complex admittances themselvescan reduce computational times.

Finally, in some embodiments, the best match to the selected subscriberline 12-14 may include a several different model lines, e.g., modellines generating errors with a below threshold value. In theseembodiments, the computer 46 identifies the selected subscriber line12-14 as having one or more common features of all of the “bestmatching” lines. For example, the computer 46 may identify the specificphysical structure of the selected subscriber line 12-14 as having abridged tap if all of the best matching model lines have a bridged tap.Then, the computer 46 may use the presence of a bridged tap incombination with other measurements to qualify or disqualify the line12-14.

FIG. 14 illustrates a method 150 of determining “best” matches by usingthe above-described phases. The computer 46 determines the length of theselected subscriber line using low frequency measurements for linecapacitance performed by the measurement unit 40 and device 43 (step152). Next, the computer 46 selects a model line having the same lengthas the selected subscriber line (step 154).

The computer 46 restricts comparisons to model lines with the samelength as the subscriber line, because physical line length affects thevalues of the phases of admittances. Limiting comparisons to this subsetof the reference set eliminates false matches with model lines whoselengths differ from the length of the selected subscriber line.

The computer 46 calculates the error function E′, based on the phase ofpreselected admittances, for the selected model line (step 155). Thecomputer 46 checks whether other model lines remain with the same length(step 156). If other lines remain, the computer 46 repeats thedetermination of E′ for another selected model line (157). If no linesremain, the computer 46 reports the model line having the smallest valuefor the error function E′ as the “best” match to the selected subscriberline (step 158).

Since the reference set may contain as many as 10,000 to 100,000 modellines, the method 150 may search the reference set hierarchically toreduce the total number of searches. In a hierarchical scheme, a firstsearch divides the reference set into non-overlapping groups of modellines. Each group has a large number of lines with similar specificphysical structures and defines one model line as a representative ofthe group. The first search uses the method 150 to determine a “best”match between the selected subscriber line and one of the representativemodel lines. A second search uses the method 150 on the model lines ofthe group associated with the best matching representative model linefound from the first search.

FIG. 15 is a flow chart illustrating a method 160 of qualifyingsubscriber lines, e.g., lines 12-14 of FIG. 1, for a high-speed dataservice, e.g., ISDN or ADSL. After selecting a subscriber line to test,the computer 46 searches a reference set of model lines for a “best”match to the selected subscriber line by using the methods 140, 150 ofFIGS. 13 and 14 (step 162). The computer 46 identifies the selectedsubscriber line as having a bridged tap or mixture of gauges in responseto the “best” match model line having a bridged tap or mixture ofgauges, respectively (step 163). The computer 46 qualifies ordisqualifies the selected subscriber line for the data service, at leastin part, based upon whether the subscriber line has a bridged tap ormixture of gauges (step 164).

In some embodiments, the computer 46 uses the signal attenuation toqualify or disqualify the selected subscriber line according to a methoddescribed in co-pending U.S. application Ser. No. 08/294,563 ('563). Inthose embodiments, the computer 46 calculates the signal attenuation bythe methods described in the '563 application. Then, the computer 46adjusts the calculated value of the signal attenuation up or downdepending on a quality factor. The quality factor depends on thespecific physical structure of the line, e.g., upon whether a bridgedtap and/or a mixture of gauges is absent or present in the subscriberline.

According to the value of the quality factor, the computer 46 adjusts acalculated signal attenuation up or down by preselected amounts. Forexample, the attenuation may be decreased, unchanged, and increased inresponse to the quality factor being good, average, and poor,respectively. Then, the computer uses the adjusted signal attenuation todetermine to qualify or disqualify the subscriber line for the dataservice according to methods described in the '563 application.

In other embodiments, the computer 46 uses some specific physical linestructures as disqualifiers. For example, if the above-described methodslead to the detection of a bridged tap, the computer 46 may disqualifythe line for the data service.

FIG. 16 is a flow chart illustrating a business method 165, which aTELCO uses to provide a high-speed data service to subscribers. TheTELCO determines which subscriber lines 12-14 of FIG. 1 are qualifiedand/or disqualified for the data service according to the method 160 ofFIG. 15 (step 166).

Using the method 160, the computer 46 determines whether linestructures, e.g., bridged taps and/or selected mixtures of gauges, arepresent. The specific physical structure is then used to adjustpredictions of electrical properties of the subscriber line, e.g., asignal attenuation. If the adjusted values of the electrical propertiesare outside of thresholds for the data service the line is disqualified.

Among subscribers with qualified lines 12-14, the TELCO determines whichsubscribers having qualified lines do not subscribe to the data service(step 167). The TELCO offers the data service to subscribers havingqualified lines and not presently subscribing to the service (step 168).

In response to finding subscribers with disqualified lines 12-14, theTELCO repairs or replaces those lines 12-14 (step 169).

Stacked Bridged Tap Detection

Referring again to FIG. 1, tests for bridged taps preferably useone-ended electrical measurements that are performed on subscriber lines12-14 via the “standard” voice test access 44. The voice test access 44acts as a low pass filter, which screens out frequencies above 20 to 100KHz. Thus, electrical measurements are generally restricted to lowfrequencies between about 20 Hz and 100 KHz.

Bridged taps manifest their presence by peaks in the signal attenuationat high frequencies, e.g., between about 200 KHz and 1,000 KHz.Predicting features of the high-frequency signal attenuation from thelow-energy measurements, which are available through the voice testaccess 44, is difficult and error prone. Present methods falsely predictthe presence or absence of bridged taps in about 40% of the cases. Falsepredictions are costly to subscribers and TELCO's, because they canresult in lost opportunities for high-speed data services and can alsoresult in investments in transmission equipment that lines do notsupport.

The accuracy of tests for line conditions and faults, e.g., bridgedtaps, can be improved with stacked generalization methods that usemultiple layers of classifiers. The classifiers determine values ofauxiliary variables, which are the labels they assign to classifysubscriber lines 12-14. Auxiliary variables are generated as outputs ofclassifiers. The auxiliary variables are thus, related to electricalmeasurements on the lines 12-14 indirectly through probabilisticrelations embodied in the classifiers. The classifiers of the stack maybe decision trees, neural networks, case-based reasoners, orstatistically based classifiers. The old electrical properties and newauxiliary variables can be combined in classifiers that provide strongcorrelations between values of these quantities and the presence orabsence of line faults and conditions, such as bridged taps and gaugemixtures.

FIG. 17 is a flow chart illustrating a method 170 for using stackedclassifiers to detect selected line conditions or faults from electricalmeasurements made with the system 11 of FIG. 1. The system 11 preferablyperforms one-ended electrical measurements on a selected subscriber line12-14 using either setup 52 or setup 60, shown in FIGS. 2A-2C, 3 (step172). To these measurements, the computer 46 applies a set of rules thatdefine a preselected set of derived electrical properties for theselected line 12-14 (step 173). Algebraic relations relate the derivedproperties to the measurements. The measured and derived electricalproperties are listed in Appendix A.

The measured and derived properties together form the input propertiesfor the stack of classifiers. These input properties may include apreliminary value of the signal attenuation, the line length, lineimpedances, and ratios of line impedances. The selection of the inputline properties for the stack can be changed to accommodate differentexpected compositions of the subscriber lines 12-14 being tested.

In each layer U, V of classifiers, shown in FIG. 17, the computer 46determines values of one or more auxiliary variables for the selectedline 12-14. The auxiliary variables may be logic-type variablesindicating that the line 12-14 is labeled by a characteristic. Theauxiliary variables may also be probability-type variables eachindicating the likelihood that the line 12-14 is labeled by one of aplurality of characteristics.

In the first layer U of the stack, the computer 46 applies a firstclassifier to input electrical measurements and properties to determinea first auxiliary variable (step 175). The first auxiliary variablecharacterizes the line 12-14 with a label “nominal” or a label“non-nominal”.

In a nominal line, low frequency properties provide a good prediction ofthe signal attenuation at the high frequencies where bridged tapsstrongly affect attenuation. Thus, knowing a value of an auxiliaryvariable that labels a line as nominal or non-nominal can improve theaccuracy of predictions about the presence of line faults like bridgedtaps.

Also in the first layer U, the computer 46 applies one or more secondclassifiers to the input electrical properties to determine one or moreother auxiliary variables (step 176). These auxiliary variables providea preliminary prediction of whether the selected line 12-14 is qualifiedor disqualified for one or more high-speed data services. In someembodiments, values of the auxiliary variables, found at step 176,indicate whether the subscriber line 12-14 is qualified for ISDN or ADSLdata services or neither.

Disqualification for high-speed data service correlates with presence ofa bridged tap, because a bridged tap lowers a line's capability to carryhigh-frequency signals. Thus, knowing a value of an auxiliary variablethat preliminarily labels a line as qualified or disqualified for datatransmissions can improve the accuracy of predictions about the presenceor absence of bridged taps.

Steps 175 and 176 may be performed in parallel or sequentially. If thesesteps 175 and 176 are sequential, the value of the auxiliary variableoutput by the earlier step may be used in the later step. If step 175 isearlier, the classifier of step 176 may use the auxiliary variablelabeling the line 12-14 as nominal or non-nominal, as an input. If step176 is earlier, the classifier of step 175 may use the auxiliaryvariables providing a preliminary qualification or disqualification fordata transmissions as inputs.

At the second layer V of the stack, the computer 46 applies a classifierto the auxiliary variables from steps 175 and 176 and the electricalmeasurements and properties from steps 172 and 173. This classifierdetermines whether the selected subscriber line 12-14 has a preselectedtype of line fault or condition (step 177). For example, the fault orcondition may be existence of a bridged tap or a gauge mixture.

The layered stack U, V can predict the presence or absence of bridgedtaps with a substantially increased accuracy. The two-layered stack ofFIG. 17 can predict the presence of bridged taps with an accuracy ofbetween about 75% and 85% and the absence of bridged taps with anaccuracy of greater than about 97%.

In steps 175, 176, and 177, classifiers analyze input data to determinethe values of output data. Henceforth, the input data, which includesone-ended measurements, properties derived from one-ended measurements,and/or auxiliary variables, are referred to as line features. The outputdata, which are values of auxiliary variables, are referred to asclassifying labels.

Their line features and labels can describe the classifiers of steps175, 176, and 177. The classifier in step 175 uses the selected measuredand derived electrical properties of the selected line 12-14 as featuresto form classes with labels “nominal” and “non-nominal”. The classifierof step 176 uses the same features to form classes with labels “ISDNqualified”, “ADSL qualified”, or “data service disqualified” in oneembodiment. The classifier of step 177 uses the same features and valuesof the characterizing labels from steps 175, 176 to form classes withlabels “bridged tap present” and “bridged tap absent”.

The label “nominal” describes a type of signal attenuation over a rangethat includes both low measurement frequencies and high data servicefrequencies. For a nominal line, the difference between actual andpredicted signal attenuations AA(f) and PA(f) has a simple dependence onfrequency “f”. The actual signal attenuation AA is the attenuation ofthe line determined from direct double-ended electrical measurements.The predicted signal attenuation PA is the attenuation obtained fromone-ended electrical measurements, e.g., using the system 11 of FIG. 1.

The predicted signal attenuation PA(f) may be obtained from a subscriberline's capacitance, e.g., the capacitance C^(tg) _(30 Hz) between tipwire and ground measured at 30 Hz. One form for the predicted signalattenuation PA(f) is:PA(f)=K(f)C ^(tg) _(30 Hz).In this formula, K(f)=−0.1729, −0.2074, −0.2395, −0.2627, and −0.2881dB/nano-Farads for respective frequencies f equal to 100, 200, 300, 400,and 500 KHz.

Another form for the predicted attenuation PA(f) is described inco-pending U.K. Patent Application 9914702.7.

For a nominal line, the difference, DFF(f), between the actual and thepredicted signal attenuations AA(f), PA(f) has one of the followingforms:

-   -   1) DFF(f)<3.5 dB for 100 KHz<f<500 KHz;    -   2) 3.5 dB≦DFF(f)<10.0 dB for 100 KHz<f<500 KHz; or    -   3) DFF(f)≧10.0 dB for 100 KHz<f<500 KHz.        If the frequency dependent difference DFF(f), i.e.,        |AA(f)−PA(f)|, does not have form 1, 2, or 3, the line 12-14 is        classified as a non-nominal line. Thus, a direct determination        of whether a particular line 12-14 is nominal requires both        one-ended and two-ended measurements to obtain both PA(f) and        AA(f).

FIG. 18A shows predicted and actual attenuations of exemplary nominallines A, B, and C. For the line A, predicted and actual attenuationsPA_(A) and AA_(A) differ by less than 3.5 dB for the entire frequencyrange between 100 and 500 KHz. The line A has a DFF(f) of form 1. Forthe line B, predicted and actual attenuations PA_(B), AA_(B) differ bybetween 4 and 9 dB over the 100 KHz to 500 KHz frequency range. The lineB has a DFF(f) of form 2. For the line C, predicted and actualattenuations PA_(C), AA_(C) differ by between more than 10.0 dB over the100 KHz to 500 KHz frequency range. The line C has a DFF(f) of form 3.

FIG. 18B shows predicted and actual attenuations of exemplarynon-nominal lines D and E. For the line D, predicted and actual signalattenuations PA_(D), AA_(D) differ by about 8 dB at 200 and 400 KHz andare equal at 150 and 300 KHz. This form for PA_(D) and AA_(D) does notcorrespond to a DFF(f) of form 1, 2, or 3. For the line E, predicted andactual signal attenuations PA_(E), AA_(E) differ by less than 3.5 dB atfrequencies between 100 and 200 KHz and by more than 8 dB at frequenciesbetween 400 and 500 KHz. This form for PA_(E) and AA_(E) also does notcorrespond to a DFF(f) of form 1, 2, or 3.

In the non-nominal lines D and E wide fluctuations occur in DFF(f).These fluctuations make a constant shift of the predicted attenuationPA(f) a poor approximation to the actual attenuation AA(f) over thewhole range that includes both high and low frequencies.

FIG. 18C shows predicted and actual signal attenuations PA_(F), AA_(F)for another nominal subscriber line F. A shifted predicted attenuationSPA_(F), which has been obtained by shifting the predicted attenuationPA_(F) by a constant, is also shown. For the nominal line F, the shiftedpredicted attenuation SPA_(F) provides a better approximation to theactual attenuation AA_(F) that the predicted attenuation PA_(F) over theentire range between 100 KHz and 500 KHz.

The actual and predicted signal attenuations AA(f), PA(f) of nominallines are approximately related by a constant shift over a widefrequency range. The wide frequency range includes both low measurementfrequencies and high frequencies where effects of bridged taps aredirectly observable.

In step 176 of FIG. 17, the labels ISDN qualified, ADSL qualified, anddata service disqualified are defined by the value of the actual signalattenuation at 100 KHz and 300 KHz. High-speed data qualified anddisqualified lines satisfy:

Class Label 100 KHz 300 KHz ADSL qualified attenuation > −47 dBattenuation > −40 ISDN qualified attenuation > −47 dB attenuation ≦ −40Disqualified attenuation ≦ −47 dB attenuation ≦ −40Thus, qualification or disqualification of a line 12-14 for ADSL andISDN are defined by the value of the actual signal attenuation at twohigh frequencies, i.e., 100 KHz and 300 KHz.

FIG. 19 illustrates a decision tree 180 that determines a classifyinglabel, e.g., an auxiliary variable, generated in steps 175-177 of FIG.17. A separate classifier, e.g., a decision tree, is used to determineeach such label.

The decision tree 180 has a hierarchical arrangement of branching tests1, 1.1-1.2; 1.1.1-2.2.2, . . . , which are grouped into descendinglevels 1, 2, 3 . . . . Each test assigns feature data received from ahigher level to disjoint subsets in the next lower level. The subsets ofthe lower level are located at ends of arrows starting at the test. Forexample, test 1.1 assigns feature data to subsets 1.1 and 1.2, which arelocated at the ends of arrows 6 and 7, see FIG. 20. At the lower level,another set of tests can act on the feature data.

FIG. 20 illustrates how the tests 1, 1.1, 1.2, . . . of the variouslevels of the decision tree 180 of FIG. 19 act on a set of feature dataassociated with the subscriber lines 12-14. Each successive testpartitions the set, i.e., by using values of the selected features, intoincreasingly disjoint output subsets. For example, test 1 partitions theinitial feature data into subset 1 and subset 2. The distal end of eachpath through the decision tree 180 assigns a subscriber line to a finalsubset in which the lines are primarily associated with one value of theclassifying label of the tree 180. Some decision trees 180 determine aprobability that the subscriber line 12-14 has the value of the label ofthe final subset to which it is assigned.

FIG. 21 is a flow chart for a method 190 of creating decision trees foruse as the classifiers in steps 175, 176, and 177 of FIG. 17. The method190 uses machine learning methods.

To employ machine learning, a training set of subscriber line data iscreated (step 192). The content the training set includes model lineswith different values of the labels used by the decision tree toclassify lines. If the decision tree classifies lines with the label“bridged tap present” and “bridged tap absent”, then some of the linesof the training sets will have bridged taps and some of the lines willnot have bridged taps. Similarly, in a stack of trees that classifieslines with a particular label, each tree therein is constructed from atraining set having lines with different values of the particular label.

For each line of the training set, a computer and/or operator determinesthe values of a set of potential features and the classifying labels(194).

The potential features include one-ended measured and derived electricalproperties that may be used in the tests of the decision tree. Thepotential electrical properties of one embodiment are listed in AppendixA. The potential features also include values of any auxiliary variablesthat may be used in the tests of the decision tree. For example, adecision tree used in step 177 of FIG. 17 would also include, aspotential features, auxiliary variables determining whether a line isnominal and preliminarily qualified for preselected data services.

The classifying labels are the values of the auxiliary variables outputby the decision tree. The values of these output auxiliary variablesmay, for example, include a determination of whether a line is nominal,qualified, or has a bridged tap.

Determinations of values of the classifying labels for the lines of thetraining set may use both one-ended and two-ended electricalmeasurements. For example, to classify a line of the training set asnominal or non-nominal a two-ended measurement of the actual attenuationand a one-ended measurement of the predicted attenuation are needed.Similarly, to determine the classifying label associated withqualification for data services, two-ended measurements of the actualattenuation are used. The two-ended measurements are not, however, usedas inputs in the construction of decision trees.

From the values of the potential features and classifying labels of eachline in the training set, the computer 46 recursively determines thebranching tests of the decision tree (step 196).

FIG. 22 is a flow chart for a method 200 of determining the branchingtests of the decision tree 180 shown in FIGS. 19-20. For each potentialfeature, the computer 46 constructs a test and partitions the trainingset into groups of disjoint subsets (step 202). The test associated witha feature assigns each line of the training set to subsets according toa value of that feature for the line.

The computer 46 evaluates gain ratio criteria for the partitioning ofthe training set produced by each potential feature (step 204). The gainratio criteria measures increases in consistency of line membership fordifferent values of the classification label in each subset. Thecomputer 46 uses the gain ratio criteria to find a best test and definestest 1 of the decision tree 180 to be the best test (step 206).

The computer loops back to perform steps 202, 204, and 206 for eachsubset produced by test 1 to determine the tests of level 2 of thedecision tree 180 (loop 208). In these determinations, the subsetsproduced by the best test of level 1 become training sets for findingthe tests of level 2. After performing steps 202, 204, and 206 for thesubsets 1 and 2, the computer 46 has determined the tests 1.1 and 1.2 ofthe level 2 (loop 208). The computer 46 performs loop 208 either untilfurther branches produce line classification errors below a preselectedthreshold or until no features remain.

Several methods exist for defining the best branching tests at eachlevel of the decision tree 180 of FIG. 19. The C4.5 method defines besttests as tests producing the highest values of the gain ratio criteria.The C4.5* method randomly picks the best tests from the tests whosevalues of the gain ratio criteria are within a preselected selectionpercentage of the highest value.

The C4.5* algorithm predicts probabilities that a line with features “d”will be partitioned into each final subset of the decision tree. Theprobability that the line will be in the majority final subset L is:P _(L)(d)=1−(Σ_((j not in L)) N _(j)+1)/(Σ_((i in L)) N _(i)+2).Here, N_(i) is the number of lines in subset “i”. The probability thatthe line will be in a subset “i” is:P _(i)(d)=[1−P _(L)(d)](N _(i)/Σ_((j in L)) N _(j)).In embodiments using the C4.5* algorithm, the above-describedprobabilities are the auxiliary variables used as features in the steps175-177 of FIG. 17.

Various embodiments combine the methods of detecting line faults (70,90), determining lines structures (140, 160), and stacking faultdetection (170), shown in FIGS. 7, 8, 13, 15, 17. By combining theabove-mentioned methods, these embodiments can better classifysubscriber lines according to a variety of criteria. These criteriainclude presence of line conditions and faults, line speed, andqualification status.

Other embodiments are within the scope of the following claims.

1. A method of testing a subscriber line, comprising: performing aone-ended electrical measurements on the subscriber line at frequenciesbelow a threshold; classifying the subscriber line based on theone-ended electrical measurements into one of at least two categoriesindicating the accuracy of predictions on the operation of the line athigher frequencies based on lower frequency measurements; predicting aperformance characteristic of the subscriber line in a frequency range,including frequencies above the threshold, based on the combination ofthe category and the one-ended electrical measurements.
 2. The method ofclaim 1, a) additionally comprising the step of making a preliminaryperformance prediction of the subscriber line and wherein predicting aperformance characteristic includes predicting a performancecharacteristic of the subscriber line based on the combination of thecategory and the one-ended electrical measurements, and b) wherein theperformance characteristic is presence of a line fault.
 3. The method ofclaim 2, wherein the line fault is a bridged tap.
 4. The method of claim1, wherein classifying the subscriber line includes assigning the lineto a first category that reflects lines having a nominal attenuationversus frequency characteristic or a second category that reflects linesnot having a nominal attenuation versus frequency characteristics.
 5. Amethod of detecting a fault in a subscriber line, comprising: a) makinga preliminary classification of the line as qualified or disqualifiedfor a data service line in response to one-ended electrical measurementsthereon; b) using one-ended electrical measurements to assign the linean attenuation classification indicating the nature of the attenuationversus frequency characteristics of the line; and c) determining whetherthe line has a fault from the one-ended electrical measurements, theattenuation classification and the preliminary classification.
 6. Themethod of claim 5, wherein the fault is one of a bridged tap and a gaugechange.
 7. The method of claim 5, wherein the measurements havefrequencies of less than 100 KHz.
 8. The method of claim 5, wherein anact of classifying includes assigning the line to a class that reflectswhether the line has nominal or non-nominal attenuation versus frequencycharacteristics.
 9. The method of claim 6, wherein an act of determiningincludes performing an analysis with a classifier, input data for theclassifier including a determination of whether the line is qualified.10. A method of manufacturing telephone line test equipment including astack of classifiers for detecting line faults, comprising: selecting alearning set of subscriber lines, a portion of the lines having thefault and a portion of the lines not having the fault; and determining aform of a classifier from values of features and auxiliary variables ofthe lines in the learning set, the values of the auxiliary variablesindicating whether the associated line has an attenuation versusfrequency profile characteristic of a line in which performance of theline at higher frequencies can be inferred from lower frequencies. 11.The method of claim 10, further comprising: determining the form of asecond classifier from values of features of the lines, the secondclassifier to output values of the auxiliary variables in response toreceiving features of lines; and wherein the first and second classifieroutput different types of auxiliary variables.
 12. The method of claim10, wherein the features include one-ended electrical measurements. 13.The method of claim 10, wherein the determining further comprises:finding a best test that partitions the lines of the learning set intosubsets according to whether the fault is present or absent.
 14. Themethod of claim 10, wherein the faults are one of bridged taps and linegauge changes.
 15. The method of claim 11, wherein at least one of theauxiliary variables output by the second classifier is a preliminaryqualification status of the lines for a preselected data service.